Coincidence - Coincidences Vs. Caused Events

Coincidences Vs. Caused Events

Measuring the probability of any series of coincidences is the most common method of evaluating and determining mere coincidence or connected causality.

The mathematically naive person seems to have a more acute awareness than the specialist of the basic paradox of probability theory, over which philosophers have puzzled ever since Pascal initiated that branch of science (for the purpose of improving the gambling prospects of a philosopher friend, the Chevalier de Méré). The paradox consists, loosely speaking, in the fact that probability theory is able to predict with uncanny precision the overall outcome of processes made up out of a large number of individual happenings, each of which in itself is unpredictable. In other words, we observe a large number of uncertainties producing a certainty, a large number of chance events creating a lawful total outcome. —Arthur Koestler, The Roots of Coincidence
"... it is only the manipulation of uncertainty that interests us. We are not concerned with the matter that is uncertain. Thus we do not study the mechanism of rain; only whether it will rain."
—Dennis Lindley, "The Philosophy of Statistics", The Statistician (2000)

To establish cause and effect (causality) is notoriously difficult, expressed by the widely accepted statement "correlation does not imply causation". In statistics, it is generally accepted that observational studies can give hints, but can never establish cause and effect. With the probability paradox considered, it would seem that the larger the set of coincidences, the more certainty rises and the more it appears that there is some cause behind the effects of this large-set certainty of random, coincidental events.

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